(EBOOK PDF)Mobile Robotics Mathematics Models and Methods Kelly 1st edition by Alonzo Kelly 9781107424364 1107424364 full chapters

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• ISBN 10:1107424364  

• ISBN 13:9781107424364

• Author:Alonzo Kelly 

 Mobile Robotics
Mathematics, Models, and Methods
Mobile Robotics offers comprehensive coverage of the essentials of the field suitable for both students and practitioners. Adapted from Alonzo Kelly’s graduate and undergraduate courses, the content of the book reflects current approaches to developing effective mobile robots. Professor Kelly adapts principles and techniques from the fields of mathematics, physics and numerical methods to present a consistent framework in a notation that facilitates learning and highlights relationships between topics. This text was developed specifically to be accessible to senior level undergraduates in engineering and computer science, and includes supporting exercises to reinforce the lessons of each section. Practitioners will value Kelly’s perspectives on practical applications of these principles. Complex subjects are reduced to implementable algorithms extracted from real systems wherever possible, to enhance the real-world relevance of the text.

Mobile Robotics Mathematics Models and Methods Kelly 1st Table of contents:

 1 Introduction
1.1 Applications of Mobile Robots
1.2 Types of Mobile Robots
1.2.1 Automated Guided Vehicles (AGVs)
1.2.2 Service Robots
1.2.3 Cleaning and Lawn Care Robots
1.2.4 Social Robots
1.2.5 Field Robots
1.2.6 Inspection, Reconnaissance, Surveillance, and Exploration Robots
1.3 Mobile Robot Engineering
1.3.1 Mobile Robot Subsystems
1.3.2 Overview of the Text
1.3.3 Fundamentals of Wheeled Mobile Robots
1.3.4 References and Further Reading
1.3.5 Exercise
2 Math Fundamentals
2.1 Conventions and Definitions
2.1.1 Notational Conventions
2.1.2 Embedded Coordinate Frames
2.1.3 References and Further Reading
2.2 Matrices
2.2.1 Matrix Operations
2.2.2 Matrix Functions
2.2.3 Matrix Inversion
2.2.4 Rank-Nullity Theorem
2.2.5 Matrix Algebra
2.2.6 Matrix Calculus
2.2.7 Leibnitz’ Rule
2.2.8 References and Further Reading
2.2.9 Exercises
2.3 Fundamentals of Rigid Transforms
2.3.1 Definitions
2.3.2 Why Homogeneous Transforms
2.3.3 Semantics and Interpretations
2.3.4 References and Further Reading
2.3.5 Exercises
2.4 Kinematics of Mechanisms
2.4.1 Forward Kinematics
2.4.2 Inverse Kinematics
2.4.3 Differential Kinematics
2.4.4 References and Further Reading
2.4.5 Exercises
2.5 Orientation and Angular Velocity
2.5.1 Orientation in Euler Angle Form
2.5.2 Angular Rates and Small Angles
2.5.3 Angular Velocity and Orientation Rates in Euler Angle Form
2.5.4 Angular Velocity and Orientation Rates in Angle-Axis Form
2.5.5 References and Further Reading
2.5.6 Exercises
2.6 Kinematic Models of Sensors
2.6.1 Kinematics of Video Cameras
2.6.2 Kinematics of Laser Rangefinders
2.6.3 References and Further Reading
2.6.4 Exercises
2.7 Transform Graphs and Pose Networks
2.7.1 Transforms as Relationships
2.7.2 Solving Pose Networks
2.7.3 Overconstrained Networks
2.7.4 Differential Kinematics Applied to Frames in General Position
2.7.5 References and Further Reading
2.7.6 Exercises
2.8 Quaternions
2.8.1 Representations and Notation
2.8.2 Quaternion Multiplication
2.8.3 Other Quaternion Operations
2.8.4 Representing 3D Rotations
2.8.5 Attitude and Angular Velocity
2.8.6 References and Further Reading
2.8.7 Exercises
3 Numerical Methods
3.1 Linearization and Optimization of Functions of Vectors
3.1.1 Linearization
3.1.2 Optimization of Objective Functions
3.1.3 Constrained Optimization
3.1.4 References and Further Reading
3.1.5 Exercises
3.2 Systems of Equations
3.2.1 Linear Systems
3.2.2 Nonlinear Systems
3.2.3 References and Further Reading
3.2.4 Exercises
3.3 Nonlinear and Constrained Optimization
3.3.1 Nonlinear Optimization
3.3.2 Constrained Optimization
3.3.3 References and Further Reading
3.3.4 Exercises
3.4 Differential Algebraic Systems
3.4.1 Constrained Dynamics
3.4.2 First- and Second-Order Constrained Kinematic Systems
3.4.3 Lagrangian Dynamics
3.4.4 Constraints
3.4.5 References and Further Reading
3.4.6 Exercises
3.5 Integration of Differential Equations
3.5.1 Dynamic Models in State Space
3.5.2 Integration of State Space Models
3.5.3 References and Further Reading
3.5.4 Exercises
4 Dynamics
4.1 Moving Coordinate Systems
4.1.1 Context of Measurement
4.1.2 Change of Reference Frame
4.1.3 Example: Attitude Stability Margin Estimation
4.1.4 Recursive Transformations of State of Motion
4.1.5 References and Further Reading
4.1.6 Exercises
4.2 Kinematics of Wheeled Mobile Robots
4.2.1 Aspects of Rigid Body Motion
4.2.2 WMR Velocity Kinematics for Fixed Contact Point
4.2.3 Common Steering Configurations
4.2.4 References and Further Reading
4.2.5 Exercises
4.3 Constrained Kinematics and Dynamics
4.3.1 Constraints of Disallowed Direction
4.3.2 Constraints of Rolling Without Slipping
4.3.3 Lagrangian Dynamics
4.3.4 Terrain Contact
4.3.5 Trajectory Estimation and Prediction
4.3.6 References and Further Reading
4.3.7 Exercises
4.4 Aspects of Linear Systems Theory
4.4.1 Linear Time-Invariant Systems
4.4.2 State Space Representation of Linear Dynamical Systems
4.4.3 Nonlinear Dynamical Systems
4.4.4 Perturbative Dynamics of Nonlinear Dynamical Systems
4.4.5 References and Further Reading
4.4.6 Exercises
4.5 Predictive Modeling and System Identification
4.5.1 Braking
4.5.2 Turning
4.5.3 Vehicle Rollover
4.5.4 Wheel Slip and Yaw Stability
4.5.5 Parameterization and Linearization of Dynamic Models
4.5.6 System Identification
4.5.7 References and Further Reading
4.5.8 Exercises
5 Optimal Estimation
5.1 Random Variables, Processes, and Transformation
5.1.1 Characterizing Uncertainty
5.1.2 Random Variables
5.1.3 Transformation of Uncertainty
5.1.4 Random Processes
5.1.5 References and Further Reading
5.1.6 Exercises
5.2 Covariance Propagation and Optimal Estimation
5.2.1 Variance of Continuous Integration and Averaging Processes
5.2.2 Stochastic Integration
5.2.3 Optimal Estimation
5.2.4 References and Further Reading
5.2.5 Exercises
5.3 State Space Kalman Filters
5.3.1 Introduction
5.3.2 Linear Discrete Time Kalman Filter
5.3.3 Kalman Filters for Nonlinear Systems
5.3.4 Simple Example: 2D Mobile Robot
5.3.5 Pragmatic Information for Kalman Filters
5.3.6 Other Forms of the Kalman Filter
5.3.7 References and Further Reading
5.3.8 Exercises
5.4 Bayesian Estimation
5.4.1 Definitions
5.4.2 Bayes’ Rule
5.4.3 Bayes’ Filters
5.4.4 Bayesian Mapping
5.4.5 Bayesian Localization
5.4.6 References and Further Reading
5.4.7 Exercises
6 State Estimation
6.1 Mathematics of Pose Estimation
6.1.1 Pose Fixing versus Dead Reckoning
6.1.2 Pose Fixing
6.1.3 Error Propagation in Triangulation
6.1.4 Real Pose Fixing Systems
6.1.5 Dead Reckoning
6.1.6 Real Dead Reckoning Systems
6.1.7 References and Further Reading
6.1.8 Exercises
6.2 Sensors for State Estimation
6.2.1 Articulation Sensors
6.2.2 Ambient Field Sensors
6.2.3 Inertial Frames of Reference
6.2.4 Inertial Sensors
6.2.5 References and Further Reading
6.2.6 Exercises
6.3 Inertial Navigation Systems
6.3.1 Introduction
6.3.2 Mathematics of Inertial Navigation
6.3.3 Errors and Aiding in Inertial Navigation
6.3.4 Example: Simple Odometry-Aided Attitude and Heading Reference System
6.3.5 References and Further Reading
6.3.6 Exercises
6.4 Satellite Navigation Systems
6.4.1 Introduction
6.4.2 Implementation
6.4.3 State Measurement
6.4.4 Performance
6.4.5 Modes of Operation
6.4.6 References and Further Reading
6.4.7 Exercises
7 Control
7.1 Classical Control
7.1.1 Introduction
7.1.2 Virtual Spring-Damper
7.1.3 Feedback Control
7.1.4 Model Referenced and Feedforward Control
7.1.5 References and Further Reading
7.1.6 Exercises
7.2 State Space Control
7.2.1 Introduction
7.2.2 State Space Feedback Control
7.2.3 Example: Robot Trajectory Following
7.2.4 Perception Based Control
7.2.5 Steering Trajectory Generation
7.2.6 References and Further Reading
7.2.7 Exercises
7.3 Optimal and Model Predictive Control
7.3.1 Calculus of Variations
7.3.2 Optimal Control
7.3.3 Model Predictive Control
7.3.4 Techniques for Solving Optimal Control Problems
7.3.5 Parametric Optimal Control
7.3.6 References and Further Reading
7.3.7 Exercises
7.4 Intelligent Control
7.4.1 Introduction
7.4.2 Evaluation
7.4.3 Representation
7.4.4 Search
7.4.5 References and Further Reading
7.4.6 Exercises
8 Perception
8.1 Image Processing Operators and Algorithms
8.1.1 Taxonomy of Computer Vision Algorithms
8.1.2 High-Pass Filtering Operators
8.1.3 Low-Pass Operators
8.1.4 Matching Signals and Images
8.1.5 Feature Detection
8.1.6 Region Processing
8.1.7 References and Further Reading
8.1.8 Exercises
8.2 Physics and Principles of Radiative Sensors
8.2.1 Radiative Sensors
8.2.2 Techniques for Range Sensing
8.2.3 Radiation
8.2.4 Lenses, Filters, and Mirrors
8.2.5 References and Further Reading
8.2.6 Exercises
8.3 Sensors for Perception
8.3.1 Laser Rangefinders
8.3.2 Ultrasonic Rangefinders
8.3.3 Visible Wavelength Cameras
8.3.4 Mid to Far Infrared Wavelength Cameras
8.3.5 Radars
8.3.6 References and Further Reading
8.3.7 Exercises
8.4 Aspects of Geometric and Semantic Computer Vision
8.4.1 Pixel Classification
8.4.2 Computational Stereo Vision
8.4.3 Obstacle Detection
8.4.4 References and Further Reading
8.4.5 Exercises
9 Localization and Mapping
9.1 Representation and Issues
9.1.1 Introduction
9.1.2 Representation
9.1.3 Timing and Motion Issues
9.1.4 Related Localization Issues
9.1.5 Structural Aspects
9.1.6 Example: Unmanned Ground Vehicle (UGV) Terrain Mapping
9.1.7 References and Further Reading
9.1.8 Exercises
9.2 Visual Localization and Motion Estimation
9.2.1 Introduction
9.2.2 Aligning Signals for Localization and Motion Estimation
9.2.3 Matching Features for Localization and Motion Estimation
9.2.4 Searching for the Optimal Pose
9.2.5 References and Further Reading
9.2.6 Exercises
9.3 Simultaneous Localization and Mapping
9.3.1 Introduction
9.3.2 Global Consistency in Cyclic Maps
9.3.3 Revisiting
9.3.4 EKF SLAM for Discrete Landmarks
9.3.5 Example: Autosurveying of Laser Reflectors
9.3.6 References and Further Reading
9.3.7 Exercises
10 Motion Planning
10.1 Introduction
10.1.1 Introducing Path Planning
10.1.2 Formulation of Path Planning
10.1.3 Obstacle-Free Motion Planning
10.1.4 References and Further Reading
10.1.5 Exercises
10.2 Representation and Search for Global Path Planning
10.2.1 Sequential Motion Planning
10.2.2 Big Ideas in Optimization and Search
10.2.3 Uniform Cost Sequential Planning Algorithms
10.2.4 Weighted Sequential Planning
10.2.5 Representation for Sequential Motion Planning
10.2.6 References and Further Reading
10.2.7 Exercises
10.3 Real-Time Global Motion Planning: Moving in Unknown and Dynamic Environments
10.3.1 Introduction
10.3.2 Depth-Limited Approaches
10.3.3 Anytime Approaches
10.3.4 Plan Repair Approach: D* Algorithm
10.3.5 Hierarchical Planning
10.3.6 References and Further Reading
10.3.7 Exercise
Index
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